You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
160 lines
6.1 KiB
160 lines
6.1 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
from paddle.fluid import core
|
|
from paddle.fluid.op import Operator
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.layers as layers
|
|
|
|
|
|
class LAMBOptimizer(paddle.optimizer.Lamb):
|
|
def _append_optimize_op(self, block, param_and_grad):
|
|
assert isinstance(block, fluid.framework.Block)
|
|
block.program._use_lamb = True
|
|
|
|
m = moment1 = self._get_accumulator(self._moment1_acc_str,
|
|
param_and_grad[0])
|
|
v = self._get_accumulator(self._moment2_acc_str, param_and_grad[0])
|
|
beta_1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
|
|
param_and_grad[0])
|
|
beta_2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
|
|
param_and_grad[0])
|
|
|
|
beta_1 = layers.fill_constant(
|
|
dtype='float32', shape=[1], value=self._beta1, name='lamb_beta_1')
|
|
beta_2 = layers.fill_constant(
|
|
dtype='float32', shape=[1], value=self._beta2, name='lamb_beta_2')
|
|
epsilon = layers.fill_constant(
|
|
dtype='float32', shape=[1], value=self._epsilon, name='epsilon')
|
|
|
|
one = paddle.ones(shape=[1]).astype('float32')
|
|
zero = paddle.zeros(shape=[1]).astype('float32')
|
|
|
|
next_m = paddle.multiply(m, beta_1) + paddle.multiply(param_and_grad[1],
|
|
one - beta_1)
|
|
next_v = paddle.multiply(v, beta_2) + paddle.multiply(
|
|
paddle.pow(param_and_grad[1], 2), one - beta_2)
|
|
|
|
beta1_correction = one - beta_1_pow_acc
|
|
beta2_correction = one - beta_2_pow_acc
|
|
|
|
next_m_unbiased = next_m / beta1_correction
|
|
next_v_unbiased = next_v / beta2_correction
|
|
|
|
update = next_m_unbiased / (paddle.sqrt(next_v_unbiased) + epsilon)
|
|
|
|
if self._exclude_from_weight_decay_fn is not None and self._exclude_from_weight_decay_fn(
|
|
param_and_grad[0]):
|
|
self._lamb_weight_decay = 0.0
|
|
update += self._lamb_weight_decay * param_and_grad[0]
|
|
|
|
w_norm = paddle.norm(param_and_grad[0], p=2)
|
|
g_norm = paddle.norm(update, p=2)
|
|
|
|
learning_rate = self._create_param_lr(param_and_grad)
|
|
|
|
ratio = paddle.where(
|
|
paddle.greater_than(w_norm, zero),
|
|
paddle.where(
|
|
paddle.greater_than(g_norm, zero), (w_norm / g_norm), one), one)
|
|
update_with_lr = ratio * learning_rate * update
|
|
next_param = param_and_grad[0] - update_with_lr
|
|
|
|
beta_1_pow_acc *= beta_1
|
|
beta_2_pow_acc *= beta_2
|
|
|
|
paddle.assign(next_m, m)
|
|
paddle.assign(next_v, v)
|
|
paddle.assign(next_param, param_and_grad[0])
|
|
|
|
return None
|
|
|
|
|
|
class TestLambOpV2(unittest.TestCase):
|
|
def test_lamb_op(self):
|
|
shape = [2, 4, 8, 8]
|
|
data = paddle.to_tensor(np.random.random(size=shape).astype("float32"))
|
|
conv = paddle.nn.Conv2D(4, 6, (3, 3))
|
|
data = conv(data)
|
|
loss = paddle.mean(data)
|
|
opt = paddle.optimizer.Lamb(
|
|
learning_rate=1e-5, epsilon=1e-8, parameters=conv.parameters())
|
|
loss.backward()
|
|
opt.minimize(loss)
|
|
|
|
assert loss.numpy() is not None
|
|
|
|
|
|
class TestLambOpWithCombinedOp(unittest.TestCase):
|
|
def test_lamb_op_with_multi_steps(self):
|
|
paddle.enable_static()
|
|
|
|
def _build_static_model(main, startup, seed=100):
|
|
with fluid.program_guard(main, startup):
|
|
main.random_seed = seed
|
|
startup.random_seed = seed
|
|
x = fluid.layers.data(name='X', shape=[13], dtype='float32')
|
|
y = fluid.layers.data(name='Y', shape=[1], dtype='float32')
|
|
prediction = fluid.layers.fc(input=x, size=1, act=None)
|
|
loss = fluid.layers.square_error_cost(input=prediction, label=y)
|
|
avg_loss = fluid.layers.mean(loss)
|
|
return avg_loss
|
|
|
|
place = fluid.CPUPlace()
|
|
num_steps = 10
|
|
|
|
for i in range(num_steps):
|
|
feed_x = np.random.random(size=(10, 13)).astype('float32')
|
|
feed_y = np.random.random(size=(10, 1)).astype('float32')
|
|
|
|
main_program = fluid.Program()
|
|
startup_program = fluid.Program()
|
|
with fluid.program_guard(main_program, startup_program):
|
|
avg_loss = _build_static_model(main_program, startup_program)
|
|
lamb_kernel = paddle.optimizer.Lamb(learning_rate=0.2)
|
|
lamb_kernel.minimize(avg_loss)
|
|
|
|
executor = fluid.Executor(place)
|
|
executor.run(startup_program)
|
|
output = executor.run(program=main_program,
|
|
feed={'X': feed_x,
|
|
'Y': feed_y},
|
|
fetch_list=[avg_loss.name])
|
|
|
|
main = fluid.Program()
|
|
startup = fluid.Program()
|
|
with fluid.program_guard(main, startup):
|
|
loss = _build_static_model(main, startup)
|
|
lamb = LAMBOptimizer(learning_rate=0.2)
|
|
lamb.minimize(loss)
|
|
|
|
exe = fluid.Executor(place)
|
|
exe.run(startup)
|
|
out = exe.run(program=main,
|
|
feed={'X': feed_x,
|
|
'Y': feed_y},
|
|
fetch_list=[loss.name])
|
|
|
|
self.assertTrue(np.allclose(out, output))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|